Trading Analyst

Company Overview

Swish Analytics is a sports analytics, betting, and fantasy startup building the next generation of predictive sports data products. We believe that odds making is a challenge rooted in engineering, mathematics, and sports expertise, not intuition. We are looking for team-oriented individuals with an authentic passion for accurate, predictive, real-time data who can execute in a fast-paced, creative, and continually evolving environment without sacrificing technical excellence.

Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building high-performance pricing and trading systems.

Swish is looking for a highly analytical Sports Trading Analyst to help strengthen and scale our sports pricing and trading operation.

In this role, you will work at the intersection of sports intelligence, quantitative modelling, pricing strategy, and live market behaviour. You will help manage and improve real-time pricing across a range of sports and market types, with a particular focus on market aware price discovery, risk management and the identification of actionable trading signals from market activity.

This role is suited to someone with strong quantitative reasoning, excellent decision-making under pressure, and a deep interest in how markets are formed, odds move, and how to engineer accurate pricing in the competitive sports betting environment.

You will work in a geographically dispersed team alongside experienced traders, quants, data scientists, and engineers, with colleagues based across Europe and the US.

• Monitor live sports markets and market activity in real time across a range of sports and market types
• Support the calibration and refinement of prices using market signals, statistical models, competitor benchmarking, and event-driven information
• Help improve pricing quality through the analysis of market behaviour, price sensitivity, liquidity patterns, and reaction speed to new information
• Contribute to the development, testing, and refinement of quantitative models by applying your understanding of live market dynamics and pricing behaviour
• Own and manage real-time trading risk, including exposure monitoring, liability controls, and disciplined decision-making across concurrent events
• Collaborate with engineering on trading and pricing infrastructure, including API integrations, automated monitoring, alerting, anomaly detection, and execution tooling
• Work closely with Sports Trading teams to interpret breaking news, lineups, injuries, team news, and other event-specific developments to ensure timely and accurate price updates
• Identify model discrepancies, edge cases, and structural inefficiencies in pricing workflows, escalating and documenting findings for Data Science and Data Engineering teams
• Help evaluate market opportunities, prioritise resources across sports and competitions, and improve operational processes as the trading function scales
• Detect sharp or informative market activity and ensure useful signals are fed back into Swish’s proprietary models and pricing systems
• Communicate effectively with internal Sports Trading teams responsible for maintaining and improving our core sports book pricing models

• Bachelor’s degree or higher in a quantitative or analytical discipline (Mathematics, Statistics, Computer Science, Economics, Engineering, Quantitative Finance, or similar), or equivalent practical experience
• Strong grounding in probability, statistics, and expected value, with the ability to reason clearly about fair price, uncertainty, and risk
• Hands‑on experience in sports trading, sports betting, exchange‑style environments, market‑making, quantitative trading, or other closely related domains where fast price formation and disciplined execution matter
• Strong understanding of sports betting fundamentals, including odds formats (decimal, fractional, American), implied probability conversion, expected value, and closing line value
• Demonstrated ability to make high‑quality decisions under time pressure with incomplete information during live events
• Comfortable working autonomously across global event…